Building LLM Agent Systems with AutoGen Training Course

Introduction to LLM Agent Systems

• Foundations of LLM agents and multi-agent architecture
• Overview of the AutoGen framework and its ecosystem
• Key agent roles: user proxy, assistant, function caller, and others

Setting Up and Configuring AutoGen

• Establishing the Python environment and installing dependencies
• Basics of AutoGen configuration files
• Integrating with LLM providers such as OpenAI, Azure, and local models

Designing Agents and Assigning Roles

• Exploring agent types and interaction patterns
• Setting agent goals, prompts, and operational instructions
• Managing task delegation and control flow based on roles

Implementing Function Calling and Tool Integration

• Registering functions for agent utilization
• Executing functions autonomously and collaboratively
• Integrating external APIs and Python scripts with agents

Managing Conversations and Memory

• Implementing session tracking and persistent memory
• Handling agent-to-agent messaging and token management
• Overseeing conversation context and history

Constructing End-to-End Agent Workflows

• Creating multi-step collaborative tasks (e.g., document analysis, code review)
• Simulating user-agent dialogues and decision-making processes
• Debugging and optimizing agent performance

Applications and Deployment

• Building internal automation agents for research, reporting, and scripting
• Developing external-facing bots, including chat assistants and voice integrations
• Packaging and deploying agent systems for production use

Recap and Future Pathways